Precise edge recognition for detecting objects is very important commercially, particularly in industrial applications and during the autonomous control of vehicles. The basic goal of object recognition is to distinguish the object to be detected from the background. A fundamental problem here is recognizing edges, i.e. transitions between a region which still belongs to the object and a region which can already be allocated to the background or other objects.
The edges or respectively transitions between various objects distinguish themselves by different optical features on various sides of the edges, whether these are different shades, gray-scale values or levels of brightness. In order to recognize edges, gradients can therefore be calculated, wherein the difference of the color values or gray-scale values of the neighboring pixels is calculated, for example for each pixel. If the surroundings of the examined pixel are substantially homogeneous, the gray-scale values or respectively color values will largely cancel each other out and the resulting calculated difference assumes a small value. Conversely, this difference is greater if the gray-scale values or color values of the neighboring pixels on different sides of the pixel to be examined differ more significantly. In this case, the difference will assume a higher value. One edge detection filter which implements such a difference method is the Sobel operator.
Edge detection is of particular relevance in the case of driving assistance systems, for example for recognizing lane markings. An additional difficulty arises from the perspective imaging of the region captured with a vehicle camera. Since sections of the lane markings, which are further away from the camera, are imaged smaller than sections which are located closer, various spatial regions are weighted with differing levels of significance during the application of the same filter to the entire camera image. One solution to this problem can consist of creating a so-called image pyramid. Here, the resolution of the original camera image is gradually reduced at various levels. In order to evaluate edges which are located in the vicinity of the camera, higher pyramid levels with a lower resolution can be examined. Conversely, lower pyramid levels having a higher resolution are examined in order to detect edges which are located further away. By making a suitable selection of the pyramid levels, the spatial dimensions of the spatial regions corresponding to the pixels are then similar, but not identical. A factor 2 is usually selected for the resolution ratio of two neighboring pyramid levels. In this case, the available resolution deviates from the ideal resolution by up to the root of 2.
However, additional computational power is required in order to produce the pyramids. Additionally, the reduction of the resolution is also associated with a loss of information and, therefore, lower localization and recognition precision.